System and method for analyzing and modeling content

ABSTRACT

Described herein are systems and methods for aggregating, parsing, and annotating regulatory context for use in resolving transactional inquiries. In one embodiment, a method comprises: aggregating documents from a plurality of data sources and storing the aggregated documents in a document database; selecting a first document from the document database; extracting regulatory content from the first document; parsing the regulatory content into a structured data object; identifying a substantively-relevant portion of the regulatory content in the structured data object; generating an annotation associated with the substantively-relevant portion; storing the generated annotation in an annotation database; and generating a domain-specific data structure for resolving transactional inquiries based on the annotation database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. application Ser. No.15/995,984, filed Jun. 1, 2018, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates to parsing and analyzing document text.More specifically, the present disclosure relates to extracting contentfrom aggregated text and analyzing the text to generate logical models.

BACKGROUND

The regulatory landscape is complex and rapidly evolving. Successfultrading models require a complete understanding of the global rules ofexchange, and compliant trading requires systematic, consistent, andauditable answers. The industry, however, faces numerous challenges. Dueto the nature and timing of regulatory rule making, financialinstitutions have historically built tactical internal solutions, oftensegregated by asset class and/or by regime. Typically, such solutionsoperate post-execution, are expensive to maintain, do not allow for fulltraceability, and cannot offer a single view of the full globalcross-regulatory implications of a trade. Without a single globaleligibility and obligations decision layer that operates across assetclass, it is exceedingly difficult, if not impossible, to manage asustainable, consistent, auditable, and up-to-date approach.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present disclosure,reference is now made to the accompanying drawings, in which likeelements are referenced with like numerals. These drawings should not beconstrued as limiting the present disclosure, but are intended to beexemplary only.

FIG. 1 illustrates an exemplary system architecture in accordance withan embodiment of the present disclosure.

FIG. 2 illustrates an exemplary data flow model in accordance with anembodiment of the present disclosure.

FIG. 3A is a flow diagram illustrating a method for aggregating,parsing, and annotating regulatory content in accordance with anembodiment of the present disclosure.

FIG. 3B illustrates an exemplary domain-specific data structure inaccordance with an embodiment of the present disclosure.

FIG. 4 illustrates an exemplary user interface for generating or editingan annotation in accordance with an embodiment of the presentdisclosure.

FIG. 5 is a flow diagram illustrating a method for rendering decisionsin response to a transactional inquiry in accordance with an embodimentof the present disclosure.

FIG. 6A illustrates an exemplary user interface providing visualizationof a transactional inquiry in accordance with an embodiment of thepresent disclosure.

FIG. 6B illustrates an exemplary user interface providing updatedvisualization of a transactional inquiry in accordance with anembodiment of the present disclosure.

FIG. 6C illustrates a structured data object that includes regulatorycontent in accordance with an embodiment of the present disclosure.

FIG. 7 is a diagram illustrating an autonomous vehicle utilizing a logicchip in accordance with an embodiment of the present disclosure.

FIG. 8 illustrates an exemplary data flow model for an autonomousvehicle in accordance with an embodiment of the present disclosure.

FIG. 9 is a block diagram illustrating an exemplary computer system inaccordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

Currently, practitioners are limited to ad hoc means of implementingcomputerized systems to resolve legal and regulatory inquiries.Typically, legal and regulatory text is translated by one group ofpersons (e.g., lawyers or compliance personnel) into a set of “businessrequirements,” which are then translated by different people (e.g.,business analysts) into a set of “functional requirements.” The“functional requirements” are then implemented by yet another group ofpersons (software developers) within a computer system.

The resulting systems have a number of key technical deficiencies.First, the process described above is neither systematic nor repeatable;thus, successive attempts to implement the same legal or regulatory textwill result in very different decision-making systems. Second, there isno way to systematically link each portion of the legal or regulatorytext to a part of the implementation, or vice-versa. Therefore, it isimpossible to say with certainty whether the implementation is completewith respect to the legal and regulatory text. Finally, each of thesteps described above is fraught with the potential for error; however,the problems already stated make it difficult, if not impossible, toascertain the correctness of the resulting system

The subject matter described herein solves these technical problems byintroducing well-defined, systematic, and repeatable elements asfollows: First, it adds a system for parsing legal and regulatorycontent into a structured data format annotating the content; theseannotations are themselves expressed as structured, typed data. Second,annotations are aggregated and translated into a logical model of aparticular legal or regulatory rule. Third, the resulting logical modelis executable and used to resolve regulatory inquiries via the inquiryprocessing component. Fourth, any given inquiry resolved using thatmodel is fully auditable, traceable, and visualizable. Finally, eachportion of the model is linked via one or more annotations to therelevant piece of legal and regulatory text, and each portion of legaland regulatory text is linked back to the segment of the modelimplementing it. Thus, the resulting system can be checked for bothcompleteness and correctness.

Described herein are embodiments for aggregating, parsing, andannotating regulatory content for use in resolving transactionalinquiries. Documents containing regulatory content are aggregated fromvarious sources. The regulatory content is extracted, parsed, andanalyzed to generate logical models for use in resolving thetransactional inquiries and generating audit records.

The embodiments described herein provide a robust enterpriseinfrastructure facilitating compliant and optimal trading of derivativesand other financial instruments across asset classes, global regulatoryregimes, central counterparties (CCPs), and execution platforms.

Implementation of the embodiments give rise to complex and up-to-daterule/data packages and decision-making methodologies, which may providea strategic and holistic approach to regulatory pre-trade andpost-execution decision-making with full traceability within real-timetrading systems. By modeling digitized regulatory law, completetraceability from all inquiries into human readable decision tree logicis made possible. In addition, the embodiments address globalcross-regulatory implications of a trade, and can help parties determinewho it is they can trade with, what can be traded, and where the tradingcan occur.

The embodiments of the present disclosure further facilitate regulatorycompliance, for example, by improving parties' abilities to followvetted and transparent regulatory workflows consistently and globallyacross all asset classes. Complete updates to rule models can occurwithin two to five days of regulatory change, allowing for suitable timeto fully document and test the models. The embodiments further providefull audit records and visualization capabilities to provide a robustframework for violation analysis and remediation. In addition, theembodiments provide a basis for a robust surveillance system foridentifying systemic regulatory rule avoidance.

Certain embodiments of the present disclosure further reduce dataredundancy and allow faster and more accurate processing of regulatorycontent. For example, changes in regulations and rules can be identifiedautomatically, and new information can be retrieved by detecting changeswithin known regulatory documents without retrieving and parsinginformation that was processed previously. This reduces the overallamount of aggregated data, thus reducing the amount of processing timeto generate annotations and perform logical modeling.

Certain embodiments of the present disclosure further allow for theprocessing of multiple transactional inquiries (e.g., on the order ofthousands per second). The domain-specific data structures describedherein allow for efficient transaction resolution and fastdecision-making, resulting in detailed audit records for every clientrequest. This is due in part to the generation of decisional modelsbased on the methods described herein for parsing and annotatingregulatory text, and defining a logical structure for decision makingbased on the annotations, their defined scopes, context, and logicalassociations to each other. Moreover, the domain-specific datastructures can be updated as new annotations are generated, allowing forup-to-date resolution of transactional inquiries.

Certain embodiments of the present disclosure provide for the resolutionof transactional inquiries based on mandates and scopes. In evaluating amandate, two questions are addressed: (1) whether a mandate applies tothe given scenario (referred to as “eligibility” or “transactioneligibility”), and (2) if the mandate does apply, what the obligationsare and how to fulfill them. Transaction eligibility can have differentcategories, such as product scope, bilateral party scope, unilateralparty scope, and transaction context scope.

Product scope relates to the issue of whether a given product describedin terms of its economic characteristics is in scope for a givenmandate. A negative outcome indicates that the product is out of scopefor the mandate under consideration, regardless of what parties aretrading it and under what context. That is, a transaction regarding aproduct deemed to be out of scope at this level may still be out ofscope once regardless of other transaction and party facts considered.In modeling the decision logic, inputs pertaining to product scopeinclude trade economic facts, such as, but not limited to: InternationalSwaps and Derivatives Association (ISDA) taxonomy (e.g., a regulator mayexclude an entire “commodity” asset class from the definition of OTCderivatives); spot transaction attributes (e.g., for identificationand/or exclusion of commodity transactions settled physically andstrictly within the spot settlement period); and key swap terms fordefining products under clearing or execution mandates (e.g.,currencies, maturity, floating rate option, credit index name).

Unilateral party scope may be evaluated for each counterparty involvedin a transaction independently, regardless of trade economics or anyother transaction-level facts. That is, it may be strictly evaluated atthe entity level using only facts about that party. A negative outcomefor this scope indicates that the party in question is never in scopefor the mandate of concern, regardless of what transaction it may enterinto (e.g., EMIR Article 1 paragraph 4 is an example of a negativeunilateral party scope). A positive outcome, however, indicates that theparticular mandate/regulation of interest does generally apply to theparty, but not necessarily to all transactions to which it is acounterparty. For example, a U.S. swap dealer is in scope generally forPart 43 real-time reporting obligations. However, if it is facing aninternal affiliate, then the intra-affiliate transaction is not eligiblefor Part 43 obligations.

Bilateral party scope considers a pair of trading parties to determinewhether the pair, regardless of what product they are trading and how,may fall within scope for a given mandate. A negative outcome indicatesthat the pair of parties will not be eligible for the given mandateobligation, whereas a positive outcome indicates that the pair ofparties may be eligible. Bilateral scope computation includes unilateralparty scopes for the two parties, plus additional logic. Inputs includeparty facts from both counterparties. Examples include intra-groupexemptions (e.g., certain intra-group risk transfers between affiliatesare often excluded from clearing and execution mandates) andcross-border or extraterritoriality rules (e.g., Commodity FuturesTrading Commission (CFTC) cross-border guidance considers parties on abilateral basis).

Transaction context scope relates to decisions where both trade economicattributes and party attributes are expected in the inputs. Relevantcontextual details may include the following: platform and venues (e.g.,whether a transaction is facing a clearing house; whether a transactionis executed on certain specified platforms); indicators related to tradelifecycle events (e.g., portfolio compression, inter-affiliate risktransfer, or option exercise into underlying swap); place of conduct(typically occurs at this level, using results from bilateral partyscope plus trader and/or sales staff location information, provided onper-transaction basis as trade facts); party and product phase-ins;exemptions that depend upon the intent or activities of parties (e.g.,hedging and commercial purpose exemptions); and various types oftemporary relief (e.g., no-action letters).

Transaction eligibility encompasses the aforementioned product scope,bilateral party scope, and transaction context scope to determineeligibility (i.e., whether a transaction is in scope for the mandatedobligations). If a transaction is deemed eligible for a given mandate,the obligations are determined. The obligations indicate what actionsare required and how to fulfill them. “Core” obligations are those thatare standardized across mandates. Available facts include who is thebearer of the obligations, the timeframe during which each obligationmust be fulfilled, and relevant market infrastructure providers (e.g.,trade repositories for reporting mandates, clearing venues for clearingmandates, etc.).

“Extra” obligations relate to idiosyncrasies of different mandates anddifferent regulatory regimes. For example, for transaction reportingmandates, the “extra” obligations may relate to the types of reports tosubmit, references to a specific form to use in the report submission,applicable block sizes in public dissemination, and clearing orelectronic execution mandates which provide data for parties todetermine whether notional masking applies and what the value is. Theseparation between standardized “core” and mandate-specific “extra”obligations is to allow for improved processing efficiency and logicalcoherence. If a transaction is not eligible for complying with a givenmandate, no obligations will be indicated (i.e., a corresponding datastructure field for obligations will be empty).

FIG. 1 illustrates an exemplary system architecture 100, in accordancewith an embodiment of the present disclosure. The system architecture100 includes a data store 110, client devices 120A-120Z, data servers130A-130Z, and a management server 140, with each device of the systemarchitecture 100 being communicatively coupled via a network 105. One ormore of the devices of the system architecture 100 may be implementedusing computer system 900, described below with respect to FIG. 9.

In one embodiment, network 105 may include a public network (e.g., theInternet), a private network (e.g., a local area network (LAN) or widearea network (WAN)), a wired network (e.g., Ethernet network), awireless network (e.g., an 802.11 network or a Wi-Fi network), acellular network (e.g., a Long Term Evolution (LTE) network), routers,hubs, switches, server computers, and/or a combination thereof. Althoughthe network 105 is depicted as a single network, the network 105 mayinclude one or more networks operating as a stand-alone networks or incooperation with each other. The network 105 may utilize one or moreprotocols of one or more devices to which they are communicativelycoupled. The network 105 may translate to or from other protocols to oneor more protocols of network devices.

In one embodiment, the data store 110 may include one or more of ashort-term memory (e.g., random access memory), a cache, a drive (e.g.,a hard drive), a flash drive, a database system, or another type ofcomponent or device capable of storing data. The data store 110 may alsoinclude multiple storage components (e.g., multiple drives or multipledatabases) that may also span multiple computing devices (e.g., multipleserver computers). In some embodiments, the data store 110 may becloud-based. One or more of the devices of system architecture 100 mayutilize their own storage and/or the data store 110 to store public andprivate data, and the data store 110 may be configured to provide securestorage for private data. In some embodiments, the data store 110 may beused for data back-up or archival purposes.

The client devices 120A-120Z may each include computing devices such aspersonal computers (PCs), laptops, mobile phones, smart phones, tabletcomputers, netbook computers, etc. Client devices 120A-120Z may also bereferred to as “user devices” or “mobile devices”. An individual usermay be associated with (e.g., own and/or use) one or more of the clientdevices 120A-120Z. The client devices 120A-120Z may each be owned andutilized by different users at different locations. As used herein, a“user” may be represented as a single individual. However, otherembodiments of the present disclosure encompass a “user” being an entitycontrolled by a set of users and/or an automated source. For example, aset of individual users federated as a community in a company orgovernment organization may be considered a “user”.

The client devices 120A-120Z may each implement user interfaces122A-122Z, respectively. Each of the user interfaces 122A-122Z may allowa user of the respective client device 120A-120Z to send/receiveinformation to/from each other, the data store 110, one or more of thedata servers 130A-130Z, and the management server 140. For example, oneor more of the user interfaces 122A-122Z may be a web browser interfacethat can access, retrieve, present, and/or navigate content (e.g., webpages such as Hyper Text Markup Language (HTML) pages) provided by themanagement server 140. As another example, one or more of the userinterfaces 122A-122Z may enable data visualization with their respectiveclient device 120A-120Z. In one embodiment, one or more of the userinterfaces 122A-122Z may be a standalone application (e.g., a mobile“app”, etc.), that allows a user of a respective client device 120A-120Zto send/receive information to/from each other, the data store 110, oneor more of the data servers 130A-130Z, and the analysis server 130.FIGS. 4, 6A, and 6B provide examples of user interfaces in accordancewith the embodiments described herein, and are discussed in greaterdetail below.

The client devices 120A-120Z may each utilize local data stores124A-124Z, respectively. Each of the local data stores 124A-124Z may beinternal or external devices, and may include one or more of ashort-term memory (e.g., random access memory), a cache, a drive (e.g.,a hard drive), a flash drive, a database system, or another type ofcomponent or device capable of storing data. The local data stores124A-124Z may also include multiple storage components (e.g., multipledrives or multiple databases) that may also span multiple computingdevices (e.g., multiple server computers). In some embodiments, thelocal data stores 124A-124Z may be used for data back-up or archivalpurposes.

In one embodiment, the data servers 130A-130Z may each include one ormore computing devices (such as a rackmount server, a router computer, aserver computer, a personal computer, a mainframe computer, a laptopcomputer, a tablet computer, a desktop computer, etc.), data stores(e.g., hard disks, memories, databases), networks, software components,and/or hardware components from which content items and metadata may beretrieved/aggregated. In some embodiments, one or more of the dataservers 130A-130Z may be a server utilized by any of the client devices120A-120Z or the management server 140 to retrieve/access content orinformation pertaining to content (e.g., content metadata).

In some embodiments, the data servers 130A-130Z may serve as sources ofcontent that can be provided to any of the devices of the systemarchitecture 100. The data servers 130A-130Z may host various types ofcontent, including, but not limited to, editable online encyclopediaarticles, online news articles, online forums, and video content. Insome embodiments, the data servers 130A-130Z may specialize inparticular types of content (e.g., a first content server that hostsvideo content, another content server that hosts online articles, etc.).In some embodiments, one or more of the data servers 130A-130Z may hostshared content, private content (e.g., content restricted to use by asingle user or a group of users), commercially distributable content,etc. In some embodiments, one or more of the data servers 130A-130Z maymaintain content databases, which can include records of content titles,descriptions, keywords, cross-references to related content orassociated content, and metadata (e.g., describing edits or updates tothe content). In some embodiments, one or more of the data servers130A-130Z may be associated with or maintained by government entities,which may make the content publicly available for retrieval. In someembodiments, the content includes documents containing regulatorycontent, which may be available in formats such as PDF, HTML, XML, orother suitable document formats.

In one embodiment, the management server 140 may include one or morecomputing devices (such as a rackmount server, a router computer, aserver computer, a personal computer, a mainframe computer, a laptopcomputer, a tablet computer, a desktop computer, etc.), data stores(e.g., hard disks, memories, databases), networks, software components,and/or hardware components that may be used to retrieve and processdocuments in accordance with the various embodiments described herein.The management server 140 includes an inquiry processing component 150,a regulations processing component 160, and a decision engine 170, whichwill be described below in detail with respect to FIG. 2.

FIG. 2 is a block diagram illustrating an exemplary data flow model 200in accordance with an embodiment of the present disclosure. The dataflow model 200 illustrates the data flow between the client device 120A,the data server 130, and the management server 140. In certainembodiments, the user interface 122A of the client device 120A mayutilize a visualization tool in order to visualize data generated by thevarious embodiments described herein. For example, the visualizationtool may comprise software installed on the client device 120A, or maybe implemented as a web interface. The visualization tool may allow auser of the client device 120A to visualize rule implementation, viewaudit records (e.g., as illustrated in FIGS. 6A and 6B) to visualize howdecisions were made along with the data used to render the decisions,compare versions of rules, and allow for human review of ruleimplementation, rule vetting, and decision validation. The annotationtool of the user interface 122A may allow for generation andmodification of annotations associated with regulatory text, as isillustrated in FIG. 4. In some embodiments, the management server 140may also implement a visualization tool to provide similar functionalityto operators of the management server 140.

In one embodiment, inquiry processing component 150 may receive aninquiry directly from the client device 120A. The inquiry may be atransactional inquiry that a user of the client device 120A seeks toresolve. The inquiry processing component 150 may evaluate and parse theinputs using a data input module, and may retrieve decisional logic forresolving the inquiry from the decision engine 170. Once the inquiry isresolved (e.g., obligations of a party are determined), the validationmodule may check the decisional logic for accuracy, and in someembodiments may provide an authorized operator of the management server140 with an opportunity to review the decisional logic. The audit recordmodule may be used to generate one or more audit records which detailpath through the decisional logic and records all decisions made andinputs used. The audit record may be transmitted to the client device120A for storage in its local data store 124A. In some embodiments, theaudit record may be stored, alternatively or additionally, in otherlocations, such as the data store 110 or in a storage device of themanagement server 140.

In one embodiment, the management server 140 may utilize the regulationsprocessing component 160 to aggregate documents from the data server130A using a data collection module. The regulations processingcomponent 160 may utilize a digitization module to extract regulatorycontent from the documents and store them, for example, in a structureddata format. The annotation module is then used to generate annotationsassociated with regulatory content extracted from the documents andstore and maintain the annotations in an annotation database. Using thedecision engine 170, the management server 140 may model rules andregulations as decisional logic based on the generated annotations,which may be derived from multiple documents across multiple datasources. Details of how decisional logic is generated and how atransactional inquiry is processed will be described below with regardto FIGS. 3 and 5.

Although each of the data store 110, the client devices 120A-120Z, thedata servers 130A-130Z, and the management server 140 are depicted inFIG. 1 as single, disparate components, these components may beimplemented together in a single device or networked in variouscombinations of multiple different devices that operate together. Insome embodiments, some or all of the functionality of the managementserver 140 may be performed by one or more of the client devices120A-120Z, or other devices that are under control of the managementserver 140. For example, the client device 120A may implement a softwareapplication that performs the functions of the inquiry processingcomponent 150, the regulations processing component 160, and/or thedecision engine 170.

FIG. 3A is a flow diagram illustrating a method 300 for aggregating,parsing, and annotating regulatory content in accordance with anembodiment of the present disclosure. The method 300 may be performed byprocessing logic that includes hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (e.g.,instructions run on a processing device to perform hardware simulation),or a combination thereof. In one embodiment, the method 300 may beperformed by a processing device executing one or more of the inquiryprocessing component 150, the regulations processing component 160, orthe decision engine 170 described with respect to FIGS. 1 and 2. In someembodiments, the method 300 is executed by one or more processingdevices of a server (e.g., the management server 140).

Referring to FIG. 3A, the method 300 begins at block 310 where aprocessing device (e.g., a processing device of the management server140 executing a regulations processing component 260) aggregatesdocuments from a plurality of data sources (e.g., one or more of thedata servers 130A-130Z), and stores the aggregated documents in adocument database (e.g., the data store 110 or other storage managed andmaintained by the management server 140). Each document may includeassociated metadata that is also stored in the document database. Forexample, the metadata for a particular document may include, but is notlimited to, a title of the document, a human-readable description orsummary of the document, a source URL, a document format, instructionsas to how the document should be aggregated (e.g., download the documentand compare to another, compare specific content within an HTML page,etc.), indicators utilized for observing changes (e.g., HTML CSSselectors), a document version number, and/or a hash value. In someembodiments, the data sources include publicly available data sourcesassociated with government entities, clearinghouses, or other entitiesthat are sources of regulatory information and guidance.

In some embodiments, the documents comprise regulatory content such asrules and regulations pertaining to financial instruments (e.g., theEuropean Union's MiFID-II regulatory framework). Regulatory content mayinclude legal text pertaining to law and regulations promulgated bygovernments, as well as rules of venues that regarding particularproducts that may be submitted for clearing, internal corporatepolicies, or other documents that have regulatory effect or provideguidance as to how legal text is interpreted. In some embodiments, themetadata for one or more of the documents may also include a taxonomy ofa particular mandate to which the document applies. In some embodiments,the plurality of data sources from which to aggregate the documents maybe based on a target regulation category. For example, the processingdevice may be select data sources associated with a particularregulation or mandate. In other embodiments, the documents may notpertain to regulatory content in the context of financial instruments.For example, the documents may contain content describing laws,regulations, and rules governing the operation of autonomous orsemi-autonomous vehicles, such as driverless cars and drone aircraft, asis discussed in detail with respect to FIG. 7.

In some embodiments, the processing device performs the aggregationperiodically. For example, the processing device may access aconfiguration file that provides instructions for when the processingdevice should check access each data source. The configuration file mayalso log when sources have been accessed. In some embodiments, theconfiguration file may contain information describing which documentshave been retrieved from the data sources, including, but not limitedto, URLs, document titles, and document version information. In someembodiments, the configuration file may specify that new or modifieddocuments should be downloaded automatically. In some embodiments, theprocessing device may transmit a notification to personnel (e.g.,individuals authorized to access the management server 140) when new ormodified documents are available for processing. The notification maycontain information descriptive of the new or modified documents, suchas a title, version, URL, and date of availability.

In some embodiments, the processing device may utilize an HTML contentcomparison to determine whether new or modified documents are available.For example, particular documents to observe may be identified using CSSselectors. For a given document, the configuration file may list allcontent in the document nodes that are specified in the document'smetadata. Using this information, the processing device may ping thedata source to determine if the document is modified or a new documentbecomes available at the data source, and in response retrieve and storethe document in the document database.

In some embodiments, the processing device may identify one or moredocuments available from the plurality of data sources that haveassociated hash values (e.g., MD5 hash values) that differ from hashvalues associated with any of the documents previously stored in thedocument database, and then retrieve the one or more documents forstorage in the document database. In some embodiments, for a particulardocument in the document database, the processing device may check for acopy of that document at its data source using a URL specified in themetadata of the document. The processing device may retrieve the copy ofthe document and compute its hash value. If the hash value is identicalto that of the document from the document database, no further action istaken. If the hash values are determined to be different, this serves asan indicator to the processing device that the copy of the document atthe data source corresponds to an updated version of the document fromthe document database. In some embodiments, the processing device thenstores the updated version of the document in the document database, andmay process the document (as discussed below) to extract any new contentpresent.

At block 320, the processing device selects a document from the documentdatabase. The processing device then extracts regulatory content fromthe first document. The document, as well as the other documents in thedocument database, may be expressed in one of many formats, such as PDF,HTML, XML, or any other suitable document format known to those ofordinary skill in the art. In some embodiments, if the documentcomprises an image of text, text may be identified within the imageusing optical character recognition, and the identified text may bestored in the metadata of the document.

At block 330, the processing device parses the regulatory content into astructured data object representative of the content of the document.The structured data object may be expressed in a structured data format,such as Akoma Ntoso format. In other embodiments, other suitablestructured data formats may be used. In some embodiments, the structureddata format is independent of the format of the document. For example,the structured data format will express the parsed regulatory content inthe same way regardless of whether the document from which theregulatory content is extracted was in PDF, HTML, or XML format.

At block 340, the processing device identifies a substantively-relevantportion of the regulatory content of the structured data object. As usedherein, the term “substantively-relevant” refers to a type ofalphanumeric data that is readable and understandable by a human readerand from which legal context can be derived, such as a legal rule, adefinition, a reference to another legal rule, and commentary. In someembodiments, identifying the substantively-relevant portion comprisesidentifying tags or other identifiers that indicate a location of aspecific portion of the content within the structured data object. Insome embodiments, the processing device may identify thesubstantively-relevant portion by determining that a particular portionof content in the structured data object corresponds to content that wasnot present in a prior version of a document in the document database.For example, the processing device may compare content extracted from anupdated version of a document to that of an older version of thedocument to determine if content has been added, deleted, or modified,and identify the added, deleted, or modified content as thesubstantively-relevant portion.

At block 350, the processing device generates an annotation associatedwith the substantively-relevant portion and stores the annotation in anannotation database. In some embodiments, multiple annotations may begenerated for and associated with the document, which may eachcorrespond to various substantively-relevant portions identifiedtherein.

The annotation database may include annotations associated with thevarious documents in the document database. In certain embodiments, eachannotation may comprise one or more rule-specific indicators, such as anindication of a group or individual responsible for generating theannotation (e.g., server-side or client-side), taxonomy of a rule, ascope to which an associated rule applies, a classification of theannotation, definition of a legislative term, a definition of a dataelement, a cross-reference to another annotation, or expressions ofrelationships between entities, between data elements, or between anentity and a data element. A “classification” of an annotation may be adescriptor of whether the associated legal provision is constitutive(e.g., a provision associated with creation, definition, or attribution)or regulative (e.g., a provision that prescribes a duty/obligation or aright, establishes a prohibition, or grants a permission). The taxonomyof a rule comprises information that may be used to classify andidentify rules based on the rule's promulgator or regulator, aparticular geographical region to which it applies, general products towhich the rule applies, and mandate names. For example, the taxonomy maybe used to identify specific rules that apply to a particular product ina particular geographical area. In some embodiments, the annotationincludes a scope to which the associated rule applies, which mayindicate a particular product to which the rule applies, whether therule applies to a unilateral party (for each party involved in thetransaction), whether the rule applies to a bilateral party (for partiesconsidered together), a transaction context, or party obligations.

In some embodiments, the processing device may automatically extract therules-specific indicators (and thus at least portions of the annotationsthemselves) from the substantively-relevant portion. The processingdevice may utilize a natural language processing algorithm to identifythe rule-specific indicators in the substantively-relevant portion. Forexample, the processing device may determine that, based the use ofparticular language, grammar, and notation, particular text of thesubstantive-relevant portion corresponds to a legal definition.

In some embodiments, an annotation for the substantively-relevantportion may be generated in response to receiving a user input (e.g.,from an individual authorized to access the management server 140 or aclient-side user utilizing one of the client-devices 120A-120Z). Forexample, a user may specifically request to generate the annotation,edit a pre-existing annotation, or delete an annotation. FIG. 4illustrates an exemplary user interface 400 for generating or editing anannotation. The user interface 400 includes a text region 402 fordisplaying regulatory text. The displayed regulatory text, in someembodiments, may be a visual representation of formatted regulatorycontent stored in the structured data object.

Within the text region 402, a substantively-relevant portion 404 of thetext is identified. In some embodiments, a user of the user interface400 may select the substantively-relevant portion 404. In otherembodiments, the substantively-relevant portion 404 may have beenautomatically identified. Once identified, the user interface 400 mayprovide an annotation window 406 in order to generate an annotation thatis to be associated with the substantively-relevant portion 404. Theannotation window includes various options, such as a name field 408 toallow the user to name the annotation, a list of fields 410 forspecifying various parameters of the annotation, such as those discussedthroughout this disclosure. Although certain parameters are illustratedin the annotation window 406, it is to be understood that those shownare not exhaustive. In some embodiments, some of the parameters may beautomatically populated (e.g., by the decision engine 170). For example,the processing device of the management server 140 may identify adefined term and associated definition in the substantively-relevantportion 404, and may populate the appropriate fields 410 with thisinformation. The user may select a cancel button 412 to delete theannotation or discard edits, or select a confirmation button 414 to savethe annotation (e.g., in the annotation database).

Referring once again to FIG. 3A, at block 360, the processing devicegenerates a domain-specific data structure that is derived at leastpartially from the generated annotation and one or more annotations ofthe annotation database. In some embodiments, the domain-specific datastructure is based at least partially on logical relationships betweenthe generated annotation and one or more annotations of the annotationdatabase. For a given mandate or regulation, the domain-specific datastructure models eligibility and obligations. Eligibility may be dividedinto scopes (e.g., product scope, unilateral party scope, bilateralparty scope, and transaction context scope), as discussed previouslyherein, with each scope being represented as a list of one or moreinclusions and/or one or more exclusions. Inclusions are propositionsabout a particular product that render it in scope, while exclusions arepropositions about the product that render it out of scope. An exemplarydomain-specific data structure 370 is shown in FIG. 3B, whichillustrates how various scopes are represented in terms of inclusionsand exclusions for a given mandate.

The processing device may generate the domain-specific data structure byidentifying annotations based on their associated scopes, and logicallylinking the information contained in the annotations. Thedomain-specific data structure may thus be utilized as an executablemodel for determining eligibility and scopes for a given mandate or rulebased in response to a list of input parameters pertaining to details ofa particular transaction.

In some embodiments, the processing device may receive a request toresolve a transactional inquiry to determine obligations of one or moreparties associated with a transaction. The request may comprise aplurality of transaction-specific parameters, such as a particularfinancial product, geographical location information, and detailsassociated various parties and their relationships to each other. Theprocessing device then generates an indication of the obligations of theone or more parties based on the domain-specific data structure usingthe plurality of transaction-specific parameters as inputs.

FIG. 5 is a flow diagram illustrating a method 500 for renderingdecisions in response to a transactional inquiry in accordance with anembodiment of the present disclosure. The method 500 may be performed byprocessing logic that includes hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (e.g.,instructions run on a processing device to perform hardware simulation),or a combination thereof. In one embodiment, the method 500 may beperformed by a processing device executing one or more of the inquiryprocessing component 150, the regulations processing component 160, orthe decision engine 170 described with respect to FIGS. 1 and 2. In someembodiments, the method 500 is executed by one or more processingdevices of a server (e.g., the management server 140).

Referring to FIG. 5, the method 500 begins at block 510 where theprocessing device receives a request to resolve a transactional inquiry,for example, to determine obligations of one or more parties associatedwith the transaction. In some embodiments, the request is received froma client device (e.g., one of the client devices 120A-120Z). The requestmay include a plurality of transactions-specific parameters, including,but not limited to, party names, geographical information, mandate type,and asset class. In some embodiments, the inquiry may specify individualscopes to evaluate. For example, the request may indicate that a productscope should be evaluated. In some embodiments, the request may specifypre-computed scopes.

At block 520, the processing device generates an indication ofobligations of the one or more parties associated with the transactionbased on a domain-specific data structure. The domain-specific datastructure may be computed, for example, based on the method of claim300. Eligibility may be determined at the scope level for each scopeassociated with a particular mandate, or for particular scopesidentified in the request. The processing device first determines, basedon logical conditions defined by the domain-specific data structure,whether a particular product or event satisfies scope conditions. If theprocessing device determines that the product or event is in scope, theprocessing device then identifies obligations of the one or more partiesunder a particular rule modeled by the domain-specific data structure.The obligations, for example, may be indications of actions to perform,locations for which the action is to be performed. In some embodiments,if additional data is required for resolving the inquiry, such asmissing information (e.g., if a geographical location of a particularparty is missing from the request), the processing device might queryavailable data sources to identify the missing information andautomatically retrieve such information. In some embodiments, thedomain-specific data structure is compiled into a different format, suchas a machine-readable format, prior to use by the processing device.

At block 530, the processing device computes data descriptive of anaudit record for visualizing a decision flow for resolving thetransactional inquiry. During the analysis performed by the processingdevice, a path taken through the rules based on decisional logic encodedin the domain-specific data structure may be logged in real-time, witheach decision resolved being stored as part of the audit record datathat is used for visualization.

At block 540, the processing device transmits data generated by theprocessing device to the client device for visualization (e.g., usingone or more of user interfaces 122A-122Z) and storage (e.g., using oneor more of local data stores 124A-124Z). The data may include, forexample, a summary of all input data provided and, for each mandate inthe request, a list of decisions expressing eligibility either for allscopes of a given mandate or for each scope specified in the request andany determined obligations. In some embodiments, if the processingdevice determines that no parties have any obligations, the data willinclude an indication that the parties have no obligations. The data mayalso include the data descriptive of the audit record, including anyreference data consumed in the analysis and all results.

In some embodiments, visualization of the data may be performed by theprocessing device (e.g., server-side visualization), with the userinterface of the client device (e.g., one or more of user interfaces122A-122Z), or both. FIGS. 6A and 6B illustrate an exemplary userinterface 600 providing visualization of a transactional inquiry inaccordance with an embodiment of the present disclosure. Referring toFIG. 6A, the user interface 600 includes a decision tree 602 thatillustrates outcomes of the decisional logic of the domain-specific datastructure to resolve a transactional inquiry. The decision tree 602includes a start node 604, indicating the beginning of the decisionalprocess and the order in which other nodes are visited. Decisional nodes606 and 608 and result nodes 610 and 612 are also present in thedecision tree 602. Node 618 may serve as an indicator of additionaldecisional logic that is hidden from view so as to not complicate theuser interface 600. In some embodiments, a user selection of the node618 may expand the decision tree 602 or display a different portion ofthe decision tree 602.

Each node is connected by paths 614 and 616. Paths 614, indicated asbold lines, correspond to paths that are actually traced out as a resultof decision resolution. The path 614A, for example, connects the startnode 604 and the decisional node 606 because the decisional node 606 maycorrespond to the first decision to be resolved for a particularmandate, and thus path 614A is the only path possible between these twonodes. Nodes 614B and 614C illustrate a logical path traced throughnodes 606 and 608 to the node 616, for example, in response to adetermination that the answer to the questions in the decisional nodes606 and 608 is in the negative. The paths 616A and 616B represent pathsnot taken which, for example, would have led to the result nodes 610 ifthe answer to the question in the decisional node 606 was in theaffirmative, or would have led to the results node 612 if the answer tothe question in the decisional node 606 was in the negative but theanswer to the question in the decisional node 608 was in theaffirmative.

In some embodiments, the decision tree 602 may be updated to includeadditional decisional logic. This may occur, for example, in thescenario where the management server 140 identifies a new or updateddocument and generates one or more additional annotations, and theseannotations, when processed, result in additional decisional logicintegrated into an associated domain-specific data structure. This isillustrated in FIG. 6B, where the decision tree 602 has been updated toinclude an additional decisional node 620. As illustrated here, thedecisional logic may result in a different outcome when executed for thesame transactional inquiry. For example, the path 614D indicates thatthat the decisional logic reaches the result node 622 instead of thedecisional node 606, for example, because the answer to the questionassociated with the decisional node 620 was in the affirmative. The path616C indicates the path that would have reached the result node 612 hadthe answer been in the negative.

In some embodiments, a user may select (e.g., via mouse click) one ormore of the nodes to receive information associated with the decisionallogic. For example, clicking on the decisional node 608 may result in adisplay of transactional parameters and scopes used in evaluating thedecision.

FIG. 6C illustrates a structured data object 650 that includesregulatory content in accordance with an embodiment of the presentdisclosure. Regulatory content may be formatted in accordance with aparticular structured data object format. The underlined and bold textrepresents newly parsed content, which may have been identified andextracted from an updated document (a substantively-relevant portion ofcontent, as described with respect to FIG. 3A). For example, the newcontent may be deemed substantively-relevant because it was not presentin a previous version of the document or the domain-specific datastructure. The new content may then serve as the basis for a newannotation, which may result in an updated domain-specific data objecthaving additional decisional logic encoded therein (e.g., as illustratedby the introduction of decisional node 620 of FIG. 6B).

For simplicity of explanation, the methods of this disclosure aredepicted and described as a series of acts. However, acts in accordancewith this disclosure can occur in various orders and/or concurrently,and with other acts not presented and described herein. Furthermore, notall illustrated acts may be required to implement the methods inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the methods couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be appreciated that themethods disclosed in this specification are capable of being stored onan article of manufacture to facilitate transporting and transferringsuch methods to computing devices. The term “article of manufacture”, asused herein, is intended to encompass a computer program accessible fromany computer-readable device or storage media.

Although embodiments of the present disclosure were discussed in termsof processing regulatory data, the embodiments may also be generallyapplied to any system in which data scraping and parsing of largequantities of documents into decisional models is applicable. Thus,embodiments of the present disclosure are not limited to regulatorycontent.

FIG. 7 is a diagram illustrating an autonomous vehicle 700 utilizing alogic chip 710 in accordance with an embodiment of the presentdisclosure. The autonomous vehicle 700 illustrated in FIG. 7 is depictedas an autonomous drone, but it is contemplated that other types ofautonomous vehicles may be utilized, such as driverless automobiles. Theautonomous vehicle 700 includes a housing 702, which surrounds andprotects a control unit 708 and the logic chip 710. Motors 704A-D arecoupled to the housing 702, which, when activated, drive the movement ofthe autonomous vehicle 700. For example, the motors 704A-D may haverotors coupled thereto that can be controlled to impart thrust andturning force to the autonomous vehicle 700.

The control unit 708 may be any type of computing device suitable foruse in an autonomous vehicle, and may include one or more of the devicesof the computer system 900, described below with respect to FIG. 9. Themotors 704A-D are operatively coupled to the control unit 708, whichtransmits signals to each individual motor 704A-D to activate/deactivatethem and control how much power they receive. Sensors 708 are coupled tothe body 702, which may include any of a light sensor, a sound sensor,an inertial measurement unit, or any other suitable sensor useful in thecontrol of the autonomous vehicle 700, as would be appreciated by one ofordinary skill in the art. The sensors 708 may be used to generatefeedback signals that are provided to and processed by the control unit706. The signals may include, but are not limited to, altitude,translational velocity, translational acceleration, rotational velocity,rotational acceleration, temperature, humidity, audio, video, lightintensity, or any other measurable signal that may facilitate theoperation of the autonomous vehicle 700.

The logic chip 710 may be any type of computing device suitable for usein an autonomous vehicle, and may include one or more of the devices ofthe computer system 900, described below with respect to FIG. 9. In someembodiments, the logic chip 710 may be a separate device from thecontrol unit 708, or may be embedded within the control unit 708. Insome embodiments, the logic chip 710 includes an application specificintegrated circuit (ASIC) that is configured to provide at least some ofthe functionality of the management server 140 described with respect toFIG. 1. For example, as illustrated in the data flow model 800 of FIG.8, the logic chip 710 may implement an inquiry processing component 850and a decision engine 870, which may be the same as or similar to theidentically named components described with respect to FIGS. 1 and 2.The logic chip 710 may further include a data store 810 for storingrules describing decisional logic for resolving inquiries and forstoring audit records. In some embodiments, the audio records are notstored on the logic chip 710, and may be stored, for example, by thecontrol unit 708 or on a remote device (e.g., transmitted to the remotedevice by the control unit 708).

The logic chip 710 is operatively coupled to the control unit 706. Insome embodiments, the control unit 706 may transmit signals receivedfrom the sensors 708 to the logic chip 710 in processed or unprocessedform. The control unit 708 may extract data from the signals which maybe stored as variables that define a state space for the operation ofthe autonomous vehicle 700. For example, the states can be discrete orcontinuous, and may include, but are not limited to, physicalcharacteristics such as position (e.g., relative to a static referenceframe or other moving objects), velocity, acceleration, and orientation.The states may also include legal designation or statuses, such aswhether the autonomous vehicle 700 is located in a restricted airspace,the observation of traffic signals, etc. In some embodiments, the statesmay include states of other autonomous vehicles, non-autonomousvehicles, and other objects. The control unit 708 may be configured forsending information regarding its own states to other devices andreceiving information regarding the states of other devices via anetwork (e.g., the network 105).

In some embodiments, the logic chip 710 is stateless (i.e., agnostic tothe current or past states of the control unit 706). In otherembodiments, the logic chip 710 may be stateful (i.e., retains orutilizes data related to a current or past state). In some embodiments,when the control unit 706 detects a change in state (e.g., a delta inone or more of the variables), at least a subset of the stateinformation is transmitted to the logic chip 710 as an inquiry (similarto the transactional inquiries discussed herein). The logic chip 710 mayresolve the inquiry by determining whether the new state is permittedbased on a domain-specific data structure specific to resolving suchinquiries. The domain-specific data structure may define scopes derivedfrom laws, rules, and regulations pertaining to the operation ofautonomous vehicles. For example, if a change in state corresponds to achange in altitude, the logic chip 710 may determine that thatparticular altitude within a particular geographic region is notpermitted, and may transmit an indication to the control unit 706, whichin turn adjusts the altitude until the logic chip 710 determines thatthe current altitude is permissible. For each transaction resolved, thelogic chip 710 may generate an audit record, which may be stored, forexample, by the logic chip 710 or the control unit 706, and/or may betransmitted to a remote device for storage (e.g., in real-time). Theimplementation of the stateless logic chip 710 as an ASIC having encodedthereon the decisional logic as described herein may dramaticallyimprove the speed of the inquiry resolution compared to, for example, anon-board general purpose processing device or a system wherein thecontrol unit 706 communicates with a remote device or system to evaluatethe actions of the autonomous vehicle 700. In some embodiments, therules utilized by the logic chip 710 may be updated. The rules may beupdated, for example, without replacing the logic chip 710 or requiringthat the update be performed locally by personnel. In such embodiments,the updated rules may be transmitted wirelessly to the logic chip 710from a remote source, and subsequently stored in the data store 810.Updates to the rules may be performed efficiently, in some embodiments,by overwriting or replacing specific rules without affecting other rulesthat are not to be changed.

FIG. 9 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 900 within which a set ofinstructions (e.g., for causing the machine to perform any one or moreof the methodologies discussed herein) may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client machine inclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein. Some or all of the components of thecomputer system 900 may be utilized by or illustrative of any of thedata store 110, one or more of the client devices 120A-120Z, one or moreof the data servers 130A-130Z, and the management server 140.

The exemplary computer system 900 includes a processing device(processor) 902, a main memory 904 (e.g., read-only memory (ROM), flashmemory, dynamic random access memory (DRAM) such as synchronous DRAM(SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 906 (e.g., flashmemory, static random access memory (SRAM), etc.), and a data storagedevice 920, which communicate with each other via a bus 910.

Processor 902 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 902 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 902 mayalso be one or more special-purpose processing devices such as an ASIC,a field programmable gate array (FPGA), a digital signal processor(DSP), network processor, or the like. The processor 902 is configuredto execute instructions 926 for performing the operations and stepsdiscussed herein.

The computer system 900 may further include a network interface device908. The computer system 900 also may include a video display unit 912(e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), or atouch screen), an alphanumeric input device 914 (e.g., a keyboard), acursor control device 916 (e.g., a mouse), and a signal generationdevice 922 (e.g., a speaker).

Power device 918 may monitor a power level of a battery used to powerthe computer system 900 or one or more of its components. The powerdevice 918 may provide one or more interfaces to provide an indicationof a power level, a time window remaining prior to shutdown of computersystem 900 or one or more of its components, a power consumption rate,an indicator of whether computer system is utilizing an external powersource or battery power, and other power related information. In someembodiments, indications related to the power device 918 may beaccessible remotely (e.g., accessible to a remote back-up managementmodule via a network connection). In some embodiments, a batteryutilized by the power device 918 may be an uninterruptable power supply(UPS) local to or remote from computer system 900. In such embodiments,the power device 918 may provide information about a power level of theUPS.

The data storage device 920 may include a computer-readable storagemedium 924 on which is stored one or more sets of instructions 926(e.g., software) embodying any one or more of the methodologies orfunctions described herein. The instructions 926 may also reside,completely or at least partially, within the main memory 904 and/orwithin the processor 902 during execution thereof by the computer system900, the main memory 904 and the processor 902 also constitutingcomputer-readable storage media. The instructions 926 may further betransmitted or received over a network 930 (e.g., the network 105) viathe network interface device 908.

In one embodiment, the instructions 926 include instructions for aexecutable component 950, which may be representative of one or more ofthe components described with respect to FIGS. 1 and 2 (e.g., theinquiry processing component 150, the regulations processing component160, and the decision engine 170). While the computer-readable storagemedium 924 is shown in an exemplary embodiment to be a single medium,the terms “computer-readable storage medium” or “machine-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterms “computer-readable storage medium” or “machine-readable storagemedium” shall also be taken to include any transitory or non-transitorymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

In preferred embodiments, disclosed herein are methods for resolvinglegal or regulatory inquiries and generating audit records, the methodscomprising: aggregating, by a processing device, documents from aplurality of data sources and storing the aggregated documents in adocument database, wherein each document comprises legal and regulatorycontent for a legal and regulatory domain; selecting, by the processingdevice, a first document from the document database, the first documentexpressed in a first format; converting, by the processing device, thefirst document into a structured data object, the structured data objectexpressed in a structured data format independent of the first format ofthe first document; generating, by the processing device, a plurality ofannotations associated with the structured data object, each annotationassociated with a data structure representing logic within the legal andregulatory content, wherein the plurality of annotations form acomprehensive data structure representing the entire logic within thelegal and regulatory content; storing, by the processing device, thegenerated annotations in an annotation database comprising annotationsassociated with the documents in the document database; transforming, bythe processing device, the structured data object into a domain-specificdata structure, the domain-specific data structure comprising a compiledexecutable logical model for decision making to resolve transactionalinquiries in the legal and regulatory domain, wherein thedomain-specific data structure is derived at least partially from one ormore logical relationships between the generated annotations and one ormore annotations of the annotation database; receiving, by theprocessing device, a request to resolve an inquiry in the legal andregulatory domain pertaining to a proposed transaction, the requestcomprising a plurality of transaction-specific parameters; executing, bythe processing device, the complied executable logical model, using theplurality of transaction-specific parameters as inputs, to resolve theinquiry; and generating, by the processing device, an audit recordcomprising a visualization of the executable logical model, how eachdecision in the executable logical model was made, and the resolution ofthe inquiry. In preferred embodiments, the request to resolve an inquiryis not search request and the proposed transaction is not a search. Infurther embodiments, the proposed transaction comprises a financialtransaction. In preferred embodiments, the entire first document isconverted into a structured data object. In preferred embodiments, theplurality of annotations are non-semantic. In further embodiments, theplurality of annotations are not generated as mark-up and are notgenerated as tags. In preferred embodiments, one or more of theannotations comprises a taxonomy of a rule, a scope to which the ruleapplies, and a classification of the annotation. In further embodiments,the one or more of the annotations further comprises one or more of adefinition of a legislative term, a definition of a data element, across-reference to another annotation, an expression of a relationshipbetween entities, or an expression of a relationship between dataelements. In preferred embodiments, generating the plurality ofannotations comprises extracting a rule-specific indicator from thelegal and regulatory content. In further embodiments, the processingdevice utilizes a natural language processing algorithm to identify therule-specific indicator within the legal and regulatory content. Inpreferred embodiments, the request to resolve a transactional inquiry inthe legal and regulatory domain comprises a request to determineobligations of one or more parties associated with the proposedtransaction, and wherein the resolution of the inquiry comprises anindication of the obligations of the one or more parties. In preferredembodiments, the method further comprises determining, by the processingdevice, that the first document corresponds to an updated version of apreviously-stored document in the document database, wherein the updatedversion corresponds to legal and regulatory content not present in thepreviously stored document. In preferred embodiments, aggregating thedocuments from the plurality of data sources comprises: identifying, bythe processing device, one or more documents available from theplurality of data sources that have associated hash values that differfrom hash values associated with any of the documents previously storedin the document database; and retrieving, by the processing device, theone or more documents and storing the one or more documents in thedocument database. In preferred embodiments, the method furthercomprises selecting, by the processing device, the plurality of datasources from which to aggregate the documents based on a targetregulation category. In preferred embodiments, the method furthercomprises periodically reaggregating, by the processing device, thedocuments from the plurality of data sources and calculating a hashvalue for each document to detect changes to the documents. In variousembodiments, the legal and regulatory content comprises one or more of:a law, a regulation, a rule, a legal text, a policy text, and a legalguidance. In a particular preferred embodiment, the visualizationcomprises a logic flow chart.

In preferred embodiments, disclosed herein are systems configured forresolving legal or regulatory inquiries and generating audit recordscomprising: a data store for maintaining a document database and anannotation database; and one or more processing devices communicativelycoupled to the data store, wherein the one or more processing devicesare configured to: aggregate documents from a plurality of data sourcesand store the aggregated documents in the document database, whereineach document comprises legal and regulatory content for a legal andregulatory domain; select a first document from the document database,the first document expressed in a first format; convert the firstdocument into a structured data object, the structured data objectexpressed in a structured data format independent of the first format ofthe first document; generate a plurality of annotations associated withthe structured data object, each annotation a data structurerepresenting logic within the legal and regulatory content, wherein theplurality of annotations form a comprehensive data structurerepresenting the entire logic within the legal and regulatory content;store the generated annotation in the annotation database comprisingannotations associated with the documents in the document database;transform the structured data object into a domain-specific datastructure, the domain-specific data structure comprising a compiledexecutable logical model for decision making to resolve transactionalinquiries in the legal and regulatory domain, wherein thedomain-specific data structure is derived at least partially from one ormore logical relationships between the generated annotations and one ormore annotations of the annotation database; receive a request toresolve an inquiry in the legal and regulatory domain pertaining to aproposed transaction, the request comprising a plurality oftransaction-specific parameters; execute the complied executable logicalmodel, using the plurality of transaction-specific parameters as inputs,to resolve the inquiry; and generate an audit record comprising avisualization of the executable logical model, how each decision in theexecutable logical model was made, and the resolution of the inquiry. Inpreferred embodiments, the request to resolve an inquiry is not searchrequest and the proposed transaction is not a search. In furtherembodiments, the proposed transaction comprises a financial transaction.In preferred embodiments, the entire first document is converted into astructured data object. In preferred embodiments, the plurality ofannotations are non-semantic. In further embodiments, the plurality ofannotations are not generated as mark-up and are not generated as tags.In preferred embodiments, one or more of the annotations comprises ataxonomy of a rule, a scope to which the rule applies, and aclassification of the annotation. In further embodiments, the one ormore of the annotations further comprises one or more of a definition ofa legislative term, a definition of a data element, a cross-reference toanother annotation, an expression of a relationship between entities, oran expression of a relationship between data elements. In preferredembodiments, generating the plurality of annotations comprisesextracting a rule-specific indicator from the legal and regulatorycontent. In further embodiments, the one or more processing devicesutilize a natural language processing algorithm to identify therule-specific indicator within the legal and regulatory content. Inpreferred embodiments, the request to resolve a transactional inquiry inthe legal and regulatory domain comprises a request to determineobligations of one or more parties associated with the proposedtransaction, and wherein the resolution of the inquiry comprises anindication of the obligations of the one or more parties. In preferredembodiments, the one or more processing devices are further configuredto determine that the first document corresponds to an updated versionof a previously-stored document in the document database, wherein theupdated version corresponds to legal and regulatory content not presentin the previously stored document. In preferred embodiments, aggregatingthe documents from the plurality of data sources comprises: identifyingone or more documents available from the plurality of data sources thathave associated hash values that differ from hash values associated withany of the documents previously stored in the document database; andretrieving the one or more documents and storing the one or moredocuments in the document database. In preferred embodiments, the one ormore processing devices are further configured to select the pluralityof data sources from which to aggregate the documents based on a targetregulation category. In preferred embodiments, the one or moreprocessing devices are further configured to periodically reaggregatethe documents from the plurality of data sources and calculating a hashvalue for each document to detect changes to the documents. In variousembodiments, the legal and regulatory content comprises one or more of:a law, a regulation, a rule, a legal text, a policy text, and a legalguidance. In a particular preferred embodiment, the visualizationcomprises a logic flow chart.

In preferred embodiments, disclosed herein are non-transitorycomputer-readable storage medium having instructions encoded thereonthat, when executed by a processing device, cause the processing deviceto resolve legal or regulatory inquiries and generate audit records byperforming at least: aggregate documents from a plurality of datasources and store the aggregated documents in a document database,wherein each document comprises legal and regulatory content for a legaland regulatory domain; select a first document from the documentdatabase, the first document expressed in a first format; convert thefirst document into a structured data object, the structured data objectexpressed in a structured data format independent of the first format ofthe first document; generate a plurality of annotations associated withthe structured data object, each annotation a data structurerepresenting logic within the legal and regulatory content, wherein theplurality of annotations form a comprehensive data structurerepresenting the entire logic within the legal and regulatory content;store the generated annotation in an annotation database comprisingannotations associated with the documents in the document database;transform the structured data object into a domain-specific datastructure, the domain-specific data structure comprising a compiledexecutable logical model for decision making to resolve transactionalinquiries in the legal and regulatory domain, wherein thedomain-specific data structure is derived at least partially from one ormore logical relationships between the generated annotation and one ormore annotations of the annotation database; receive a request toresolve an inquiry in the legal and regulatory domain pertaining to aproposed transaction, the request comprising a plurality oftransaction-specific parameters; execute the complied executable logicalmodel, using the plurality of transaction-specific parameters as inputs,to resolve the inquiry; and generate an audit record comprising avisualization of the executable logical model, how each decision in theexecutable logical model was made, and the resolution of the inquiry. Inpreferred embodiments, the request to resolve an inquiry is not searchrequest and the proposed transaction is not a search. In furtherembodiments, the proposed transaction comprises a financial transaction.In preferred embodiments, the entire first document is converted into astructured data object. In preferred embodiments, the plurality ofannotations are non-semantic. In further embodiments, the plurality ofannotations are not generated as mark-up and are not generated as tags.In preferred embodiments, one or more of the annotations comprises ataxonomy of a rule, a scope to which the rule applies, and aclassification of the annotation. In further embodiments, the one ormore of the annotations further comprises one or more of a definition ofa legislative term, a definition of a data element, a cross-reference toanother annotation, an expression of a relationship between entities, oran expression of a relationship between data elements. In preferredembodiments, generating the plurality of annotations comprisesextracting a rule-specific indicator from the legal and regulatorycontent. In further embodiments, the processing device utilizes anatural language processing algorithm to identify the rule-specificindicator within the legal and regulatory content. In preferredembodiments, the request to resolve a transactional inquiry in the legaland regulatory domain comprises a request to determine obligations ofone or more parties associated with the proposed transaction, andwherein the resolution of the inquiry comprises an indication of theobligations of the one or more parties. In preferred embodiments, theinstructions, when executed by the processing device, further cause theprocessing device to determine that the first document corresponds to anupdated version of a previously-stored document in the documentdatabase, wherein the updated version corresponds to legal andregulatory content not present in the previously stored document. Inpreferred embodiments, aggregating the documents from the plurality ofdata sources comprises: identifying one or more documents available fromthe plurality of data sources that have associated hash values thatdiffer from hash values associated with any of the documents previouslystored in the document database; and retrieving the one or moredocuments and storing the one or more documents in the documentdatabase. In preferred embodiments, the instructions, when executed bythe processing device, further cause the processing device to select theplurality of data sources from which to aggregate the documents based ona target regulation category. In preferred embodiments, theinstructions, when executed by the processing device, further cause theprocessing device to periodically reaggregate the documents from theplurality of data sources and calculating a hash value for each documentto detect changes to the documents. In various embodiments, the legaland regulatory content comprises one or more of: a law, a regulation, arule, a legal text, a policy text, and a legal guidance. In a particularpreferred embodiment, the visualization comprises a logic flow chart.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description may have been presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is herein, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the preceding discussion,it is appreciated that throughout the description, discussions utilizingterms such as “receiving”, “retrieving”, “transmitting”, “computing”,“generating”, “adding”, “subtracting”, “multiplying”, “dividing”,“selecting”, “parsing”, “optimizing”, “calibrating”, “detecting”,“storing”, “performing”, “analyzing”, “determining”, “enabling”,“identifying”, “modifying”, “transforming”, “applying”, “aggregating”,“extracting”, or the like, refer to the actions and processes of acomputer system, or similar electronic computing device, thatmanipulates and transforms data represented as physical (e.g.,electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The disclosure also relates to an apparatus, device, or system forperforming the operations herein. This apparatus, device, or system maybe specially constructed for the required purposes, or it may include ageneral purpose computer selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a computer- or machine-readable storage medium, such as, butnot limited to, any type of disk including floppy disks, optical disks,compact disk read-only memories (CD-ROMs), and magnetic-optical disks,read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Reference throughout this specification to “an embodiment” or “oneembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “anembodiment” or “one embodiment” in various places throughout thisspecification are not necessarily all referring to the same embodiment.Moreover, it is noted that the “A-Z” notation used in reference tocertain elements of the drawings is not intended to be limiting to aparticular number of elements. Thus, “A-Z” is to be construed as havingone or more of the element present in a particular embodiment.

As used herein, “transaction” refers to an interaction between twoentities that may result in a legal contract.

As used herein, “transactional inquiry” refers to a request made of theresulting inquiry processing component to evaluate whether a proposed oractual transaction is permissible under a given law or rule or set oflaws and/or rules. The inquiry therefore consists of facts about theentities involved in the transaction, the object of the transaction(e.g., a financial instrument to be created or exchanged), and how thetransaction is to be concluded (e.g., executed on an exchange orbilaterally).

As used herein, “executable logical model” refers to the fact that oncea legal and regulatory model has been produced, it may be loaded intothe inquiry processing component, which in turn receives regulatoryinquiries specifying the model to evaluate along with the relevant inputrequirements. The inquiry processing component applies the rule(s)embodied in the model in light of the information provided in theinquiry and provides a response as described herein.

As used herein, “audit record” refers to the complete informationreturned by the inquiry processing component, including: all informationprovided in the initial inquiry (i.e., the input); a complete record ofthe decision rendered (e.g., for financial regulatory inquiries, that atrade represented by the input may not proceed in accordance with therule the logical model embodies); and full information about how thedecision was rendered (e.g., why a trade may not proceed in accordancewith the rule the logical model embodies). Thus, the audit recordfacilitates the visualization and traceability provided by theembodiment described herein.

The present disclosure is not to be limited in scope by the specificembodiments described herein. Indeed, other various embodiments of andmodifications to the present disclosure, in addition to those describedherein, will be apparent to those of ordinary skill in the art from thepreceding description and accompanying drawings. Thus, such otherembodiments and modifications are intended to fall within the scope ofthe present disclosure. Further, although the present disclosure hasbeen described herein in the context of a particular embodiment in aparticular environment for a particular purpose, those of ordinary skillin the art will recognize that its usefulness is not limited thereto andthat the present disclosure may be beneficially implemented in anynumber of environments for any number of purposes. Accordingly, theclaims set forth below should be construed in view of the full breadthand spirit of the present disclosure as described herein, along with thefull scope of equivalents to which such claims are entitled

What is claimed is:
 1. A method for resolving legal or regulatoryinquiries and generating audit records, the method comprising: a)aggregating, by a processing device, documents from a plurality of datasources and storing the aggregated documents in a document database,wherein each document comprises legal and regulatory content for a legaland regulatory domain; b) selecting, by the processing device, a firstdocument from the document database, the first document expressed in afirst format; c) converting, by the processing device, the firstdocument into a structured data object, the structured data objectexpressed in a structured data format independent of the first format ofthe first document; d) generating, by the processing device, a pluralityof annotations associated with the structured data object, eachannotation associated with a data structure representing logic withinthe legal and regulatory content, wherein the plurality of annotationsform a comprehensive data structure representing the entire logic withinthe legal and regulatory content; e) storing, by the processing device,the generated annotations in an annotation database comprisingannotations associated with the documents in the document database; f)transforming, by the processing device, the structured data object intoa domain-specific data structure, the domain-specific data structurecomprising a compiled executable logical model for decision making toresolve transactional inquiries in the legal and regulatory domain,wherein the domain-specific data structure is derived at least partiallyfrom one or more logical relationships between the generated annotationsand one or more annotations of the annotation database; g) receiving, bythe processing device, a request to resolve an inquiry in the legal andregulatory domain pertaining to a proposed transaction, the requestcomprising a plurality of transaction-specific parameters; h) executing,by the processing device, the complied executable logical model, usingthe plurality of transaction-specific parameters as inputs, to resolvethe inquiry; and i) generating, by the processing device, an auditrecord comprising a visualization of the executable logical model, howeach decision in the executable logical model was made, and theresolution of the inquiry.
 2. The method of claim 1, wherein the requestto resolve an inquiry is not search request and the proposed transactionis not a search.
 3. The method of claim 2, wherein the proposedtransaction comprises a financial transaction.
 4. The method of claim 1,wherein the entire first document is converted into a structured dataobject.
 5. The method of claim 1, wherein the plurality of annotationsare non-semantic.
 6. The method of claim 5, wherein the plurality ofannotations are not generated as mark-up and are not generated as tags.7. The method of claim 1, wherein one or more of the annotationscomprises a taxonomy of a rule, a scope to which the rule applies, and aclassification of the annotation.
 8. The method of claim 7, wherein theone or more of the annotations further comprises one or more of adefinition of a legislative term, a definition of a data element, across-reference to another annotation, an expression of a relationshipbetween entities, or an expression of a relationship between dataelements.
 9. The method of claim 1, wherein generating the plurality ofannotations comprises extracting a rule-specific indicator from thelegal and regulatory content.
 10. The method of claim 9, wherein theprocessing device utilizes a natural language processing algorithm toidentify the rule-specific indicator within the legal and regulatorycontent.
 11. The method of claim 1, wherein the request to resolve atransactional inquiry in the legal and regulatory domain comprises arequest to determine obligations of one or more parties associated withthe proposed transaction, and wherein the resolution of the inquirycomprises an indication of the obligations of the one or more parties.12. The method of claim 1, further comprising determining, by theprocessing device, that the first document corresponds to an updatedversion of a previously-stored document in the document database,wherein the updated version corresponds to legal and regulatory contentnot present in the previously stored document.
 13. The method of claim1, wherein aggregating the documents from the plurality of data sourcescomprises: identifying, by the processing device, one or more documentsavailable from the plurality of data sources that have associated hashvalues that differ from hash values associated with any of the documentspreviously stored in the document database; and retrieving, by theprocessing device, the one or more documents and storing the one or moredocuments in the document database.
 14. The method of claim 1, furthercomprising selecting, by the processing device, the plurality of datasources from which to aggregate the documents based on a targetregulation category.
 15. The method of claim 1, further comprisingperiodically reaggregating, by the processing device, the documents fromthe plurality of data sources and calculating a hash value for eachdocument to detect changes to the documents.
 16. The method of claim 1,wherein the legal and regulatory content comprises one or more of: alaw, a regulation, a rule, a legal text, a policy text, and a legalguidance.
 17. The method of claim 1, wherein the visualization comprisesa logic flow chart.
 18. A system configured for resolving legal orregulatory inquiries and generating audit records, the systemcomprising: a) a data store for maintaining a document database and anannotation database; and b) one or more processing devicescommunicatively coupled to the data store, wherein the one or moreprocessing devices are configured to: i) aggregate documents from aplurality of data sources and store the aggregated documents in thedocument database, wherein each document comprises legal and regulatorycontent for a legal and regulatory domain; ii) select a first documentfrom the document database, the first document expressed in a firstformat; iii) convert the first document into a structured data object,the structured data object expressed in a structured data formatindependent of the first format of the first document; iv) generate aplurality of annotations associated with the structured data object,each annotation a data structure representing logic within the legal andregulatory content, wherein the plurality of annotations form acomprehensive data structure representing the entire logic within thelegal and regulatory content; v) store the generated annotation in theannotation database comprising annotations associated with the documentsin the document database; vi) transform the structured data object intoa domain-specific data structure, the domain-specific data structurecomprising a compiled executable logical model for decision making toresolve transactional inquiries in the legal and regulatory domain,wherein the domain-specific data structure is derived at least partiallyfrom one or more logical relationships between the generated annotationsand one or more annotations of the annotation database; vii) receive arequest to resolve an inquiry in the legal and regulatory domainpertaining to a proposed transaction, the request comprising a pluralityof transaction-specific parameters; viii) execute the compliedexecutable logical model, using the plurality of transaction-specificparameters as inputs, to resolve the inquiry; and ix) generate an auditrecord comprising a visualization of the executable logical model, howeach decision in the executable logical model was made, and theresolution of the inquiry.
 19. The system of claim 18, wherein therequest to resolve an inquiry is not search request and the proposedtransaction is not a search.
 20. The system of claim 19, wherein theproposed transaction comprises a financial transaction.
 21. The systemof claim 18, wherein the entire first document is converted into astructured data object.
 22. The system of claim 18, wherein theplurality of annotations are non-semantic.
 23. The system of claim 22,wherein the plurality of annotations are not generated as mark-up andare not generated as tags.
 24. The system of claim 18, wherein one ormore of the annotations comprises a taxonomy of a rule, a scope to whichthe rule applies, and a classification of the annotation.
 25. The systemof claim 24, wherein the one or more of the annotations furthercomprises one or more of a definition of a legislative term, adefinition of a data element, a cross-reference to another annotation,an expression of a relationship between entities, or an expression of arelationship between data elements.
 26. The system of claim 18, whereingenerating the plurality of annotations comprises extracting arule-specific indicator from the legal and regulatory content.
 27. Thesystem of claim 26, wherein the one or more processing devices utilize anatural language processing algorithm to identify the rule-specificindicator within the legal and regulatory content.
 28. The system ofclaim 18, wherein the request to resolve a transactional inquiry in thelegal and regulatory domain comprises a request to determine obligationsof one or more parties associated with the proposed transaction, andwherein the resolution of the inquiry comprises an indication of theobligations of the one or more parties.
 29. The system of claim 18,wherein the one or more processing devices are further configured todetermine that the first document corresponds to an updated version of apreviously-stored document in the document database, wherein the updatedversion corresponds to legal and regulatory content not present in thepreviously stored document.
 30. The system of claim 18, whereinaggregating the documents from the plurality of data sources comprises:identifying one or more documents available from the plurality of datasources that have associated hash values that differ from hash valuesassociated with any of the documents previously stored in the documentdatabase; and retrieving the one or more documents and storing the oneor more documents in the document database.
 31. The system of claim 18,wherein the one or more processing devices are further configured toselect the plurality of data sources from which to aggregate thedocuments based on a target regulation category.
 32. The system of claim18, wherein the one or more processing devices are further configured toperiodically reaggregate the documents from the plurality of datasources and calculating a hash value for each document to detect changesto the documents.
 33. The system of claim 18, wherein the legal andregulatory content comprises one or more of: a law, a regulation, arule, a legal text, a policy text, and a legal guidance.
 34. The systemof claim 18, wherein the visualization comprises a logic flow chart. 35.A non-transitory computer-readable storage medium having instructionsencoded thereon that, when executed by a processing device, cause theprocessing device to resolve legal or regulatory inquiries and generateaudit records by performing at least: a) aggregate documents from aplurality of data sources and store the aggregated documents in adocument database, wherein each document comprises legal and regulatorycontent for a legal and regulatory domain; b) select a first documentfrom the document database, the first document expressed in a firstformat; c) convert the first document into a structured data object, thestructured data object expressed in a structured data format independentof the first format of the first document; d) generate a plurality ofannotations associated with the structured data object, each annotationa data structure representing logic within the legal and regulatorycontent, wherein the plurality of annotations form a comprehensive datastructure representing the entire logic within the legal and regulatorycontent; e) store the generated annotation in an annotation databasecomprising annotations associated with the documents in the documentdatabase; f) transform the structured data object into a domain-specificdata structure, the domain-specific data structure comprising a compiledexecutable logical model for decision making to resolve transactionalinquiries in the legal and regulatory domain, wherein thedomain-specific data structure is derived at least partially from one ormore logical relationships between the generated annotation and one ormore annotations of the annotation database; g) receive a request toresolve an inquiry in the legal and regulatory domain pertaining to aproposed transaction, the request comprising a plurality oftransaction-specific parameters; h) execute the complied executablelogical model, using the plurality of transaction-specific parameters asinputs, to resolve the inquiry; and i) generate an audit recordcomprising a visualization of the executable logical model, how eachdecision in the executable logical model was made, and the resolution ofthe inquiry.
 36. The non-transitory computer-readable storage medium ofclaim 35, wherein the request to resolve an inquiry is not searchrequest and the proposed transaction is not a search.
 37. Thenon-transitory computer-readable storage medium of claim 36, wherein theproposed transaction comprises a financial transaction.
 38. Thenon-transitory computer-readable storage medium of claim 35, wherein theentire first document is converted into a structured data object. 39.The non-transitory computer-readable storage medium of claim 35, whereinthe plurality of annotations are non-semantic.
 40. The non-transitorycomputer-readable storage medium of claim 38, wherein the plurality ofannotations are not generated as mark-up and are not generated as tags.41. The non-transitory computer-readable storage medium of claim 35,wherein one or more of the annotations comprises a taxonomy of a rule, ascope to which the rule applies, and a classification of the annotation.42. The non-transitory computer-readable storage medium of claim 41,wherein the one or more of the annotations further comprises one or moreof a definition of a legislative term, a definition of a data element, across-reference to another annotation, an expression of a relationshipbetween entities, or an expression of a relationship between dataelements.
 43. The non-transitory computer-readable storage medium ofclaim 35, wherein generating the plurality of annotations comprisesextracting a rule-specific indicator from the legal and regulatorycontent.
 44. The non-transitory computer-readable storage medium ofclaim 43, wherein the processing device utilizes a natural languageprocessing algorithm to identify the rule-specific indicator within thelegal and regulatory content.
 45. The non-transitory computer-readablestorage medium of claim 35, wherein the request to resolve atransactional inquiry in the legal and regulatory domain comprises arequest to determine obligations of one or more parties associated withthe proposed transaction, and wherein the resolution of the inquirycomprises an indication of the obligations of the one or more parties.46. The non-transitory computer-readable storage medium of claim 35,wherein the instructions, when executed by the processing device,further cause the processing device to determine that the first documentcorresponds to an updated version of a previously-stored document in thedocument database, wherein the updated version corresponds to legal andregulatory content not present in the previously stored document. 47.The non-transitory computer-readable storage medium of claim 35, whereinaggregating the documents from the plurality of data sources comprises:identifying one or more documents available from the plurality of datasources that have associated hash values that differ from hash valuesassociated with any of the documents previously stored in the documentdatabase; and retrieving the one or more documents and storing the oneor more documents in the document database.
 48. The non-transitorycomputer-readable storage medium of claim 35, wherein the instructions,when executed by the processing device, further cause the processingdevice to select the plurality of data sources from which to aggregatethe documents based on a target regulation category.
 49. Thenon-transitory computer-readable storage medium of claim 35, wherein theinstructions, when executed by the processing device, further cause theprocessing device to periodically reaggregate the documents from theplurality of data sources and calculating a hash value for each documentto detect changes to the documents.
 50. The non-transitorycomputer-readable storage medium of claim 35, wherein the legal andregulatory content comprises one or more of: a law, a regulation, arule, a legal text, a policy text, and a legal guidance.
 51. Thenon-transitory computer-readable storage medium of claim 35, wherein thevisualization comprises a logic flow chart.